Chapter 4 – STEM: Teaching practical analytical chemistry online: improving delivery of a Year 2 NMR Spectroscopy Practical

Abstract

Practical analytical skills, including processing and analysing spectroscopic data, are vital skills for many scientists, often developed during undergraduate laboratory-based modules. During the Covid-19 lockdown, student access to labs was prohibited, but we were able to use remote access to spectrometers and other specialist software to deliver practical work online. In addition, we successfully created the community and collegiality of lab-based courses by offering improved access to demonstrators, academic staff, and their peers during synchronous online lab sessions. This chapter will discuss the logistics of setting up an analytical chemistry virtual experiment, unexpected improvements to students’ experience and writeups, and how we provided bespoke feedback sessions to each student.

Background

Acquisition of practical analytical skills is a core component of chemistry degree programmes (Salzer et al., 2005). These skills include operation of instrumentation, appropriate processing, and elucidation of spectra. To gain these skills, chemistry undergraduates need to familiarise themselves with instrument software. Chemistry students at the University of Liverpool (UoL) study analytical skills throughout their degree programmes in a variety of ways, from learning background theory in lecture-based courses, to hands-on experience in the laboratory throughout their degree. Practical skills include operation of instrumentation, appropriate processing, and elucidation of spectra. To gain these skills, chemistry undergraduates need to familiarise themselves with instrument software. This chapter focuses on this skill acquisition, taught to year 2 students in their second semester analytical chemistry lab course and its online delivery during the pandemic (Sorto et al., 2020). This is a laboratory-based module in which students practise a range of measurement techniques appropriate for the investigation of a wide range of chemical phenomena spanning thermodynamics, kinetics, spectroscopy, electrochemistry, surface science and transition metal chemistry. In one of these experiments, students learn how to acquire, process, simulate and analyse spectra using Nuclear Magnetic Resonance (NMR) spectroscopy. Part of the learning outcome of this experiment is for students to think critically about their collected data. Students are expected to ask themselves; How can the presentation of the data be improved by adjusting the processing parameters? Is my data of a suitable standard for simulations to be performed? Can I unambiguously assign the data – can I see all the information in my spectra? This method of inspection can be applied to the analysis and evaluation of any data type and our aim is for students to recognise what good data looks like.

Pre-Covid-19, this experiment comprised five tasks:

1. Prepare an NMR sample, submit the sample for NMR analysis.
2. Download and process the spectra acquired in task 1 appropriately and identify the compound present.
3. Simulate the spectra from task 2.
4. Download and process a complex set of NMR spectra. Process the data appropriately and identify the isomer molecules present.
5. Simulate the NMR spectra from task 4.

This experiment was taught during timetabled lab sessions, starting in the teaching laboratory (task 1), after which students would be supported by academic staff and PhD student demonstrators in a computer suite (tasks 2-5). This experiment would take on average 12-15 hours for students to complete. The sessions for this experiment were scheduled weekly in six three-hour slots, either in the morning or afternoon. Students often needed a lot of support from demonstrators to perform the simulations of spectra, and the demand for demonstrators’ time was high during these sessions.

During the experiment, students were required to evidence their methodology, results and discussion through screenshots and explanations. This produced lengthy documents that students were required to print ahead of a live marking session in the laboratory with a demonstrator. The experiment was graded with respect to the quality of the students’ writeup, results, and understanding.

During the marking sessions, students discussed their writeup with a demonstrator, receiving oral and written feedback. The marking sessions for this experiment routinely took 30-40 minutes.

Logistics of setting up a virtual experiment during the Covid-19 pandemic

As lockdown 2020 commenced towards the end of the delivery of this experiment (March 2020), students finishing their experiments were left with less support (emails to the module coordinator) and missed out on live feedback with a demonstrator during their marking slot. They were asked to submit an electronic copy of their report that was assessed asynchronously.

In Summer 2020, as the pandemic continued, a decision was made to run this experiment remotely. To replicate the face-to-face environment effectively, the following factors were considered:

• How will data be generated for task 1?
• How do we give access to assistance from demonstrators?
• How do we replicate the face-to-face environment?
• How do we retain the live feedback and marking session with a demonstrator?

From September 2020 to April 2021, chemistry at UoL was delivered predominantly online. Timetabled sessions were reserved for workshops, tutorials and drop-in sessions with the majority of lectures being recorded and delivered asynchronously. This allowed a degree of flexibility in the timetable, something that was taken advantage of when running virtual experiments (Jones et al., 2021). In this case, the second-year cohort was divided into four groups and each group was allocated a week to complete the NMR experiment. This was timetabled as shown in Table 1. During this week, students had no other classes timetabled so that their only task was to complete and write up this experiment.

Table 1: Exemplar timetable for NMR experiment

All timetabled sessions were delivered on Zoom. The links to each session were made available to students via the timetabling app. The introductory session consisted of a brief presentation detailing the learning outcomes of the experiment, how to access software needed for the experiment and a demonstration of data processing. For this experiment, where specialist software was needed, this was accessed through the University’s remote desktop. This allowed students to access software without the need for download, meaning issues with different operating systems were mitigated.

Students were allocated into groups of six students and each student was invited to join a Microsoft Teams channel for their group. This was set up so that students could support each other and could discuss the experiment outside of the timetabled sessions and so that peer support was available to replicate the conversations that students would usually have in the lab. Demonstrators also had access to the Teams channels so that they could monitor conversations and, if students were sharing data, that this could be monitored for collusion and plagiarism.

During the timetabled sessions on Zoom, students were allocated to breakout rooms made up of participants from their group. These sessions were hosted by academic and PhD demonstrators who were made co-hosts. An advantage of using Zoom to teach remotely is the ability to control students’ screens to demonstrate something using their data or to check their results. It was possible to do this despite students using the remote desktop. Using Zoom to demonstrate a computer-based practical increased the efficiency of demonstrators. If lots of students were asking the same question we were able to close the breakout rooms, demonstrate the concept to all students and then allow them to continue working in their breakout rooms. Students were able to call for help by pressing the help button, returning to the main room, as the members of their group, messaging the demonstrators and staff on Teams or asking to move to an additional breakout room with a demonstrator for 1:1 tuition. Having Teams and Zoom open in the background was manageable for the majority of students. On the rare occasion that students were not able to log in to the Zoom session, they were able to work with their group on Teams either during or after the session. Students were very supportive of each other both outside of the timetabled sessions and while in the breakout rooms.

After the week online, students, without prompting, emailed staff to express how well supported they felt having a demonstrator on call while they did their experiment and how being in groups was as close as they could feel to being in a real lab environment under the current circumstances.

I genuinely did really feel very supported the whole way, always felt like I had somewhere to turn to ask questions. All demonstrators were really helpful and really knew what they were talking about- and I felt very comfortable asking them questions. I also really like the idea of being in groups that was a really big help. Even if I wasn’t able to ask them any questions, just being in a group made the whole process more enjoyable. (Year 2 student)

Working in breakout rooms has also been great as I’m able to get help easily and speak to people I haven’t really spoken to before. (Year 2 student)

Assessment and feedback

Once students had submitted their experimental writeup to Canvas (Liverpool virtual learning environment), they were contacted by a demonstrator via email to arrange a 30-minute marking slot. Ahead of the marking session, the demonstrator would send a Microsoft Teams invitation and read the report. During the marking session the demonstrator and student would discuss the experiment, writeup, results, and underlying science to probe the students’ understanding of the experiment. Verbal feedback was given during these sessions in addition to a paragraph written by the demonstrator as a summary of the conversation. Students were then given three grades for writeup, results, and understanding.

Reflection

Operating this experiment as a week’s intensive experience allowed students to focus and be better supported throughout with improved access to staff and collegiality with their peers.

The year group was split into four groups, so the experiment was repeated for four consecutive weeks. Not only is this heavily labour-intensive for demonstrators to teach and mark, as we reduce social distancing and move to more face-to-face sessions we may not be able to sustain the week-long blocks of time within the constraints of the timetable.

Using the remote desktop was hugely advantageous as it was accessible to demonstrators while using Zoom; it allowed students to access all software without downloading it to their devices; students could work from anywhere, at home or on campus; and the diversity of up-to-date software, access to software, problems with firewalls etc. were mitigated.

Access to demonstrators was dramatically improved. In the face-to-face sessions, students would have to wait, with their hand up, for a demonstrator to approach them. When on Zoom, they could press the help button and a demonstrator would enter their breakout room as soon as they were available.

Students were confident in explaining concepts to each other and worked together after building a rapport with other members of their group, another example of using technology to enhance communication and interaction between peers (Erkan, 2019). They were also able to access peer support outside of the timetabled sessions on Teams. This was observed on many occasions and could be monitored by staff. The research shows the importance of working in small interactive groups to introduce students to analytical processes (Matilainen et al., 2017).

Moving forward, this experiment will remain hybrid (Enneking, 2019) with the practical (in lab) aspect returning but with support from demonstrators being virtual rather than in person in a PC suite. Depending on changes to the timetable, this experiment may need to forgo its week-long intensive timetable, but this can be scheduled over two weeks to coincide with other experiments on the module and other synchronous activities.

References

Enneking, K.M., Breitenstein, G.R., Coleman, A.F., Reeves, J.H., Wang, Y., & Grove, N.P. (2019.) The Evaluation of a Hybrid, General Chemistry Laboratory Curriculum: Impact on Students’ Cognitive, Affective, and Psychomotor Learning. Journal of Chemical Education, 96(6), 1058–1067. https://doi.org/10.1021/acs.jchemed.8b00637

Erkan, A. (2019.) Impact of Using Technology on Teacher-Student Communication/Interaction: Improve Students Learning. World Journal of Education, 9(4), 30–40. https://doi.org/10.5430/wje.v9n4p30

Jones, E.V., Shepler, C.G., & Evans, M.J. (2021.) Synchronous Online-Delivery: A Novel Approach to Online Lab Instruction. Journal of Chemical Education, 98(3), 850–857. https://doi.org/10.1021/acs.jchemed.0c01365

Matilainen, R., Koliseva, A., Valto, P., & Valisaari, J. (2017.) Reconstruction of undergraduate analytical chemistry laboratory course. Analytical and Bioanalytical Chemistry, 409, 3–10. https://doi.org/10.1007/s00216-016-9953-6

Salzer, R., Mitchell, T., Mimero, P., Karayannis, M., Efstathiou, C., Smith A., & Valca´rcel, M. (2005.) Analytical chemistry in the European higher education area. Analytical and Bioanalytical Chemistry, 381(1), 33–40. https://doi.org/10.1007/s00216-004-2908-3

Valle-Suárez, R.M., Calderón-Mendoza, G.L., Lanza-Sorto N.A., & Ponce-Rodríguez H.D. (2020.) Teaching Instrumental Analytical Chemistry in the Framework of COVID-19: Experiences and Outlook. Journal of Chemical Education, 97(9), 2723–2726. https://doi.org/10.1021/acs.jchemed.0c00707